This unit (NIT2251) discusses concepts, techniques and applications of data mining and machine learning. Data mining is the computational paradigms and algorithms to discover patterns from large data sets. Data mining is one of the most advanced tools used by IT industries. Machine learning is a branch of Artificial Intelligence and is an important component of the growing field of data science. This unit covers various topics include introduction to data mining, data pre-processing and machine learning approaches such as supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Students engage in hands-on programming exercises to implement some of the fundamental algorithms such as decision trees, support vector machines, regression analysis, Bayesian networks, clustering, and deep learning to analyse real world data.

Unit details

Study level:
Credit points:
Unit code:


NIT1102 - Introduction to Programming

Learning Outcomes

On successful completion of this unit, students will be able to:
  1. Apply basic concepts and techniques of data mining to solve practical problem;  
  2. Critically evaluate advantages and disadvantages of data mining solutions on real world datasets;  
  3. Experiment and evaluate machine-learning algorithms on various benchmark datasets; and  
  4. Apply machine-learning algorithms with considerations of data privacy and professional ethics and evaluate their usefulness and useability.  


Assessment type Description Grade
Test Open book online test 10%
Test Open book online test 10%
Project Data mining project - code, and report 40%
Project Machine learning project- code, and report 40%

Where to next?

As part of a course

This unit is studied as part of the following courses. Refer to the course page for information on how to apply for the course.

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